<html>
<head>
<title> Group ICA of fMRI Toolbox Help </title>
<meta http-equiv="Content-Type" content="HTML, DHTML, CSS, XML, XHTML, JavaScript, VBScript; charset=iso-8859-1">
<link rel="stylesheet" type="text/css" href="style.css">
<style type="text/css">
.style1 {
	text-align: center;
}
</style>
</head>

<body>

<h4> Covariance Matrix Options </h4>

<hr>

These options are provided for calculating covariance matrix during the second 
and third data reduction stages. Figure 1 shows the covariance matrix options. 
Each option is explained below:

<ul>

<li> Do You Want To Stack Datasets? - Options are 'Yes' and 'No'. 


<ul> 
<li> Yes - Data sets are stacked to compute covariance matrix. This option 
assumes that there is enough RAM available to stack the data sets. Please note 
that full storage of covariance matrix is required when you select this option. </li>

<li> No - A pair of data sets are loaded at a time to compute covariance matrix. 
This option uses less memory usage but it requires N*(N-1)/2 loops to compute 
the covariance matrix where N is the number of data sets. </li>

</ul>
</li>

<li> 'Select Matrix Storage Type' - Options are 'Full' and 'Packed'. You have 
the option to store only lower triangular portion of the symmetric matrix with 
the packed storage scheme. </li>

<li> 'Select Precision' - Options are 'Double' and 'Single'. Single precision 
uses 50% less memory required when compared to double precision. Single 
precision is accurate up to 7 digits after decimal point. </li>

<li> 'Select Eigen Solver Type' - Options are 'Selective' and 'All'. These 
options will be used only for the packed storage scheme.

<ul>

<li> 'Selective' - Only a few desired eigen values are computed. This option 
will compute eigen values faster when compared to 'All' option. However, if 
there are convergence issues use option 'All' to compute eigen values.</li>

<li> 'All' - All eigen values are computed. We recommend to use this option for 
computing eigen values only when the selective eigen solver doesn't converge.</li>

</ul>
</li>
</ul>

<strong>Note:</strong> If you want a better performance during the data 
reduction stage, use full storage of covariance matrix and single precision. 
However, if you want less memory usage during the data reduction stage use 
packed storage scheme, single precision and selective eigen solver.

<p class="style1"> Figure 1: Covariance Matrix Options </p>

<p class="style1">
<img src="gift/images/icatb_covariance_options.jpg">

</p>

<hr>

</body>

</html>